Method and apparatus for detecting defects on the surface of tyres

ABSTRACT

Method and related apparatus for detecting defects on a surface of a tire, include: providing the tire; acquiring a digital image including a structure including sections representative of linear elements of a pattern in a surface portion and representative of possible elongated defects, the sections of the structure having a respective orientation; providing a model of the pattern in the surface portion, wherein each pixel is associated with a first index representative of whether the pixel belongs or not to a pattern section and a second index representative of an at least local orientation of the pattern section passing through the pixel; calculating for each pixel of the structure a third index representative of the orientation of the structure section passing through the pixel; and establishing, for each pixel of the structure having a corresponding pixel in the pattern model belonging to the pattern, whether the pixel of the structure belongs to a proposed defect on the basis of the comparison between the third index and the second index associated with the corresponding pixel in the pattern model.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a national phase application based onPCT/IB2016/053680, filed Jun. 21, 2016, and claims the priority ofItalian Patent Application No. 102015000028956, filed Jun. 30, 2015, thecontent of each application being incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to a method and an apparatus for detectingthe possible presence of defects on the surface of a tyre, where thesurface has a pattern comprising, or consisting of, a plurality ofsections.

Description of the Related Art

By the term ‘defect’ it is meant any deviation from a desired condition,irrespective of the fact that such deviation gives rise to a degradationof the performance of the tyre (which can thus be discarded ordowngraded) or consists of a simple anomaly (e.g. aesthetic) that doesnot cause the tyre to be discarded or downgraded. The defects may forexample be portions with non-vulcanised compound, alterations in shape,cuts, creeps in the carcass, presence of foreign bodies on the surface,etc.

By “tyre” it is meant the finished tyre, that is, after the moulding andvulcanisation steps. Once the green tyre has been prepared, a mouldingand vulcanisation treatment is typically carried out in order todetermine the structural stabilisation of the tyre through cross-linkingof the elastomeric compositions as well as to impart a desired treadpattern thereon and any distinguishing or information graphic signs atthe sidewalls.

According to a typical method, during the moulding and vulcanisationstep, a suitable bladder (typically of an elastomeric compound) isinserted inside the tyre and made to expand (e.g. pneumatically) againstthe inner surface of the same, in order to push the tyre against theouter mould and ensure the proper adhesion thereof to the same. Thisbladder is typically externally grooved with a pattern of grooves inorder to facilitate the local mutual sliding thereof between the innersurface of the tyre and the outer surface of the bladder duringvulcanisation and moulding. Such a pattern can facilitate the outflow ofair trapped between the bladder and the tyre and/or facilitate thedetachment between the bladder and the tyre at the end of the mouldingand vulcanisation. Therefore the inner surface of the tyre has acorresponding “pattern”, typically in relief. Typically, the patterncomprises a series of rectilinear and parallel spans typicallydistributed with substantial periodicity along the circumferentialdirection, and/or a dense network of contiguous geometric shapes (in thejargon called ‘pebble edge’).

In the present description and claims, by ‘pattern’ it is more generallymeant a set of linear elements arranged on the inner or outer surface ofa tyre, such linear elements being generated during the moulding andvulcanisation step of the tyre and being typically in relief, but beingable to also be in low relief or simply two-dimensional, i.e. at thesame elevation level as the rest of the surface. For example, the treadgrooves fall within the definition of pattern. Such surface linearelements are typically rectilinear segments interconnected in acontinuous network, but can more generally have any shape and/or surfacedistribution. Such surface linear elements typically cause a colourchange and/or reflectivity/diffusivity of an incident light compared tothe rest of the surface.

Typically, a tyre for vehicle wheels has a substantially toroidalstructure around an axis of symmetry coinciding with the axis ofrotation of the same during operation, and has an axial middle lineplane orthogonal to the axis of symmetry, said plane typically being ageometrical (substantial) symmetry plane (ignoring any minorasymmetries, such as tread pattern and/or parts of the inner structure).

By “inner surface” of the tyre it is meant the surface extending insidesaid toroidal structure from one to the other bead (in other words, thesurface no longer visible after the coupling of the tyre with therespective mounting rim).

The inner surface typically belongs to a layer of elastomeric material,usually called “liner”, having optimal air impermeabilitycharacteristics. Certain defects on the inner surface, such as cuts,open joints, creeps, etc., may impair the air impermeability of thetyre.

In the context of the production processes of tyres for vehicle wheels,the need has been felt to perform quality inspections on finishedproducts, with the aim to prevent defective tyres from being put on themarket, and/or to progressively adjust the apparatuses and machineryused so as to improve and optimise the execution of the operationscarried out in the production process.

These quality inspections include for example those performed by humanoperators who dedicate a fixed time to a visual and tactile inspectionof the tyre; if, in the light of his own experience and sensitivity, theoperator were to suspect that the tyre does not meet certain qualitystandards, the same tyre is subjected to further inspections, through amore detailed human inspection and/or suitable equipment in order todeepen the evaluation of any structural and/or qualitative deficiencies.

Document WO2013/045594A1 describes a quick method of analysis of theelements in relief on the inner surface of a tyre, comprising the stepsof: capturing a three-dimensional image of the surface assigning a greylevel value to each pixel of the image proportional to the topographicalelevation of that point to obtain a starting image, transforming thecaptured image in an orthogonal reference system (OXY) in which theabscissa axis (OX) represents the circumferential values, and theordinate axis (OY) the radial values, assigning a value of the altitudegradient (f(p)) to each pixel on the surface, comparing its elevationwith the elevation of a discrete and small number of points arranged ona straight line passing through the respective pixels (p) and orientedin the circumferential direction.

In the field of tyre quality control, the Applicant has set itself theproblem of detecting the possible presence of defects on the surface ofa tyre by the optical acquisition of digital images and subsequentprocessing thereof. The Applicant has observed for the quality controlto be used “in line” in a plant for the production of tyres, it isnecessary that the inspections itself is performed in a reduced time andwith reduced costs. In this context, the computational requirement ofthe processing algorithms plays a crucial role, since when it isexcessive, the control times increase unacceptably and/or thecomputational capacity required makes the control unfeasible.

By “digital image”, or equivalently “image”, it is generally meant a setof data, typically contained in a computer file, in which each tuple ofcoordinates (typically each pair of coordinates) of a finite set(typically two-dimensional and matrix, i.e. N rows×M columns) of tuplesof spatial coordinates (each tuple corresponding to a pixel) isassociated with a corresponding set of numerical values (which may berepresentative of different magnitudes). For example, in monochromeimages (such as those in grey levels or ‘greyscale’), such a set ofvalues consists of a single value in a finite scale (typically 256levels or tones), this value for example being representative of theluminosity (or intensity) level of the respective tuple of spatialcoordinates when displayed. A further example is represented by colourimages, in which the set of values represents the luminosity level of aplurality of colours or channels, typically the primary colours (forexample red, green and blue in RGB coding and cyan, magenta, yellow andblack in CMYK coding). The term ‘image’ does not necessarily imply theactual display of the same.

In the present description and claims, the term ‘image’ encompasses boththe three-dimensional images, in which each pixel is associated with asurface altitude information (such as the images obtained with lasertriangulation), and two-dimensional images, in which each pixel isassociated with information representative of the colour and/orreflectivity/diffusivity of the respective point of the surface, such asthe images detected by the common digital cameras or video cameras (e.g.CCD).

By ‘point of the surface’ it is meant a surface portion having a small(not zero) extension compatible with the size of a pixel of an acquiredimage of the surface.

In the present description and claims, any reference to a specific“digital image” (for example, the two-dimensional digital imageinitially acquired on the tyre) includes more generally any digitalimage obtainable through one or more digital processing of said specificdigital image (such as, for example, filtering, equalization, smoothing,finalisation, thresholding, morphological transformations (opening,etc.), derivative or integral calculations, etc.).

SUMMARY OF THE INVENTION

The Applicant has noted that the presence of the pattern on the tyresurface disturbs the image and/or its processing, in tyre qualitycontrol, since it tends to hide or mask the defects.

This the more so when the defect, for example a cut, intersects one ormore sections of the pattern, since in the intersection area it isdifficult to distinguish, by numerical processing, a pattern sectionfrom a defect, in particular if it also is elongated.

The Applicant, in the context of quality control of tyres (in particularfor the detection of defects on the surface of tyres) based on theacquisition and processing of digital images in an industrial tyreproduction line, with reduced computational costs and requirements,reliable in the result obtained and also with a high degree ofsensitivity in the detection of defects, in particular being capable ofdetecting surface defects even in the presence of a surface pattern, hastherefore considered the problem of developing a method and an apparatusfor detecting defects on the surface of tyres able to distinguish anyelongated surface defects that intersect with the sections of a patternpresent on the surface, from the sections themselves.

The Applicant has solved the above problem by the method and apparatusof the present invention that use the difference in the respectiveorientation of the elongated defects and of the pattern sections.

In a first aspect thereof, the invention relates to a method fordetecting defects on a surface of a tyre, the method comprising:

-   -   providing the tyre having said surface which has a pattern        comprising, or consisting of, a set of linear elements;    -   acquiring at least one digital image of a portion of the        surface, said digital image comprising a structure comprising        sections representative of said linear elements of the pattern        in said surface portion and representative of possible elongated        defects, said sections of said structure having, at least        locally, a respective orientation;    -   providing a digital model of the pattern in said surface        portion, wherein each pixel is associated with a first index        representative of whether the pixel belongs to a pattern section        in said model and, for the pixels belonging to the pattern in        said model, a second index representative of an at least local        orientation of said pattern section in said model passing        through said pixel;    -   calculating, for each pixel of said structure, a third index        representative of the orientation of the structure section        passing through said pixel;    -   for each pixel of the structure having a corresponding pixel in        the pattern model belonging to the pattern, comparing said third        index with said second index associated with said corresponding        pixel in the pattern model,    -   on the basis of said comparison, establishing if said pixel of        the structure belongs to a proposed defect.

According to the Applicant the above method, due in particular to theidentification of the pixel structure that are superimposed to thepattern in the pattern model (by means of the first index) and, for eachof these pixels, due to the comparison between the respectiveorientation of the structure section passing by the pixel (expressed bythe third index) with the respective orientation of a correspondingpattern section in the pattern model passing by the corresponding pixelof the pattern model (expressed by the second index), allows detectingany defect sections with an elongated shape that intersect, therebysuperimposing on, the pattern sections in the pattern model, on thebasis of the fact that the orientation of these defect sections istypically different from the orientation of the intersected patternsection in the pattern model. For example, the above method allowsdistinguishing the structure sections superimposed to the pattern in thepattern model and which are representative of the pattern in said atleast one digital image (since they have substantially the sameorientation as the corresponding pattern sections in the pattern model)from the structure sections superimposed to the pattern in the patternmodel and which are representative of elongated defects (since they havean orientation substantially different from the section pattern thatintersects it in the pattern model).

Preferably, said first index representative of whether the pixel belongsor not to a pattern section in said pattern model is binary.

Preferably, it is contemplated to identify a first set of pixels of saidat least one digital image that belong to said structure and whosecorresponding pixels in the pattern model do not belong to the pattern.Advantageously, in this way, the possible defects located outside of thepattern, i.e. belonging to the ‘background’ of the pattern, areidentified.

Preferably, it is contemplated to identify a second set of pixelscomprising said possible defects.

Preferably, it is contemplated to create a final image representative ofsaid surface portion wherein the pixels corresponding to said first andsecond set of pixels are discriminated from the remaining pixels.

Typically, acquiring said at least one digital image comprises acquiringa first digital image each pixel of which is associated with aluminosity value representative of the reflectivity and/or diffusivityand/or colour of a surface point corresponding to said each pixel.Preferably, said point on the surface is illuminated with diffusedlight.

In a first embodiment, calculating said third index representative ofthe orientation of the structure section passing through the pixelcomprises calculating a gradient in said pixel, said gradient beingcharacterized by a modulus and an orientation in said digital image.Typically, the gradient orientation is representative of the angleformed by the gradient vector with respect to a reference direction.Preferably, the orientation is normalized in a range of 180°.

Typically, said gradient is a vector with two components representativeof a variation of said luminosity values along two coordinates(typically orthogonal to each other), respectively.

The term ‘gradient’ in the present application is used in a generalsense to indicate the variation of the luminosity values along the twocoordinates, and not necessarily with reference to the differentialcalculation.

Preferably, said third index is representative of said gradientorientation.

In a second embodiment, calculating said third index representative ofthe orientation of the structure section passing by the pixel includescalculating at least one eigenvector of a Hessian matrix in the pixel.

Preferably, said third index is representative of an orientation of saidat least one eigenvector.

Preferably, said at least one eigenvector is the principal eigenvectorof the Hessian matrix. Preferably, said pattern model contains a dilatedpattern with respect to said pattern in said at least one digital imagewithout defects. In this way, advantageously, a certain tolerance isintroduced in deciding whether a pixel of the image under analysisbelongs or not to the pattern, for example to take account of possiblevariations/deformation/drifts of the pattern sections in said imageunder analysis.

Preferably, comparing said third index with said second index comprisescalculating the angular difference between the at least localorientation of the structure section passing through the pixel and theat least local orientation of the corresponding pattern section in thepattern model passing through the pixel. Preferably, the pixel isdetermined to belong to a possible defect if the angular differenceexceeds, in absolute value, 5°, more preferably 10°, even morepreferably 15°. A certain tolerance on the calculated difference isthereby advantageously introduced.

Typically, said sections representative of said linear elements of thepattern are rectilinear segments, more typically interconnected to forma network of polygons.

Preferably, said surface portion is a circumferential inner surfaceportion, more preferably corresponding to an angle in the centre that isgreater than or equal to 30°, more preferably greater than or equal to60°, typically equal to at least one round angle.

Preferably, said circumferential inner surface portion has a width in aplane passing by said axis, greater than or equal to 50 mm, morepreferably greater than or equal to 80 mm, and/or smaller than or equalto 200 mm, more preferably smaller than or equal to 150 mm. Preferably,the method is repeated by varying each time said circumferential innersurface portion so that all the surface portions make up at least onewhole inner surface half-portion that extends from the median plane to abead.

Preferably, the method comprises carrying out the analysis describedabove while keeping the tyre resting on one of the sidewalls.Preferably, the method comprises tilting the tyre so as to rest it on anopposite sidewall and repeating the operations described above.

According to a second aspect thereof, the invention relates to anapparatus for analysing tyres in a tyre production line.

The apparatus comprises:

-   -   a support for a tyre, preferably horizontal and preferably        adapted to rotate around an axis perpendicular thereto;    -   at least one source adapted to emit at least one light radiation        for illuminating a surface portion of the tyre, when set on the        support, and, at a distance from said source, a detection system        adapted to detect an optical intensity of the light radiation        reflected and/or diffused by said surface portion; and    -   a processing unit configured for actuating the method according        to the first aspect of the present invention.

Preferably, the detection system comprises a linear camera having anobjective line lying on an optical plane passing by the linear camera.

Preferably, said at least one source includes a first light source, asecond light source and a third light source adapted to emit a first, asecond and a third light radiation, respectively, for illuminating saidsurface portion, more preferably a linear surface portion coincidentwith or near the objective line.

Preferably, said first light source and second light source lie onopposite sides, respectively, with respect to said optical plane.

Preferably, each of said first and second light source is adapted toilluminate said objective line with a respective grazing light, and saidthird light source is adapted to illuminate said objective line withdiffuse light.

In one embodiment, the detection system comprises a mirror having areflective surface arranged at the third light source perpendicular tothe optical plane and intersecting the latter (typically on the medianline of the mirror) in a manner so as to reflect said objective line inthe optical plane by an angle greater than or equal to 30° or smallerthan or equal to 135°. In this way, advantageously, during theinspection of the inner surface of the tyre, the linear camera remainspositioned in the central area of the tyre while the group with thelight sources works close to the inner surface.

Preferably, the apparatus comprises a command and control unitconfigured for:

-   -   activating, in alternating sequence, said first light source,        second light source and third light source; and    -   driving said linear camera for respectively acquiring said        first, second and third image synchronously with the activation        of said first light source, second light source and third light        source, respectively. In this way, it is possible to acquire        both an image in diffuse light and two images in grazing light.

Preferably, the apparatus includes a movement member adapted to rotatesaid support, about an axis of rotation thereof, the command and controlunit being configured for controlling said movement member.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features and advantages will become more apparent from thedetailed description of some exemplary but non-limiting embodiments of amethod and an apparatus for analysing tyres in a tyre production line,according to the present invention. Such description will be givenhereinafter with reference to the accompanying figures, provided onlyfor illustrative and, therefore, non-limiting purposes, in which:

FIG. 1 shows a schematic diagram, in terms of functional blocks, of anapparatus for analysing the surface of tyres according to the presentinvention;

FIG. 2 shows a schematic view of a part of the apparatus according tothe present invention according to an embodiment variant;

FIGS. 3-14 show some steps of the method using a visual representationof respective digital images;

FIG. 15 shows a flow chart of the method of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

With reference to the figures, reference numeral 1 generally indicatesan apparatus for analysing a surface of tyres in a tyre production lineaccording to the present invention.

Apparatus 1 comprises a support 102 adapted to support tyre 200 on asidewall and to rotate the same about an axis of rotation 201 coincidingwith the axis of rotation of the tyre and typically arranged accordingto the vertical. Support 102 is typically operated by a driving member,not further described and shown, since it may exemplarily be of knowntype.

Apparatus 1 comprises a source 104 adapted to emit at least one lightradiation for illuminating a surface portion of the tyre set on thesupport, and, at a distance from said source, a detection system 105adapted to detect an optical intensity of the light radiation reflectedand/or diffused by the surface portion.

The detection system 105 comprises a camera, preferably linear andhaving an objective line 106 lying on an optical plane 107 passing bythe linear camera and the axis of rotation 201.

Source 104 comprises a first light source 108, a second light source 109and a third light source 110 adapted to emit a first, a second and athird light radiation, respectively, for illuminating a linear surfaceportion 211 of said tyre coinciding with the objective line (forexample, when the surface portion is planar) or in the vicinity of theobjective line (due to the curvilinear trend of the tyre surface).

The detection system 105 is adapted to acquire a respectivetwo-dimensional digital image of the linear surface portion of thesurface illuminated by at least one of the first, second and third lightradiation.

Typically, the apparatus comprises a robotic arm (not shown) on whichthe first, second and third light source and the detection system aremounted.

Preferably, the first light source 108 and the second light source 109consist each of a single respective sub-source 111 and 112. Preferably,the third light source 110 consists of four respective sub-sources 113distributed on both sides of the optical plane 107 and symmetricallywith respect to such plane.

Each sub-source 111-113 has a respective main direction of developmentwhich develops parallel to the optical plane 107 and thus to theobjective line 106.

Each sub-source typically comprises a plurality of LED light sourcesarranged aligned along the main direction of development.

In FIG. 2, the light sub-sources are schematically shown with referenceto their respective emitting surface (exemplarily of rectangular shape),which may for example coincide with a transparent protective and/ordiffuser glass. Exemplarily, the sub-sources have a dimension along themain direction of development equal to 6 cm and a dimension along thedirection orthogonal to the main direction of development equal to about1 cm.

Preferably, the sub-sources 111 and 112 lie on opposite sides,respectively, with respect to the optical plane and are equidistanttherefrom.

Preferably, the distance of the sub-sources 113 of the third lightsource from the optical plane 107 is smaller than the distance betweeneach sub-source of said first light source and second light source andthe optical plane.

Preferably, the third light source 110 is adapted to illuminate theobjective line with diffused light (for example a respective anglehaving its vertex in each point of the objective line and lying in aplane orthogonal to the objective line, and subtended by the third lightsource, is equal to about 80°).

In an embodiment of the apparatus particularly adapted for theinspection of the inner surface of the tyre, exemplarily shown in FIG.2, the detection system includes a mirror 150 (typically also mounted onthe robotic arm) having a flat reflective surface arranged at the thirdlight source perpendicularly to the optical plane and intersecting thelatter on the median line of the mirror, so as to reflect the objectiveline in the optical plane by an angle exemplarily equal to 90°.

Preferably, a command and control unit 140 is comprised, configured toactivate in an alternating sequence the first, second and third lightsource, and control the linear camera for acquiring a first, second andthird image, respectively, in synchronization with the activation of thefirst, second and third light source, respectively.

The command and control unit is typically configured to also control thehandling member of support 102.

The apparatus comprises a processing unit (for example integrated in thecommand and control unit 140 or in communication therewith or with thedetection system 105 for receiving said acquired images) configured forimplementing the method according to the present invention.

In operation, a tyre 200 is placed on support 102 and subjected to a(preferably full) rotation around its axis of symmetry 201 in order todetect a two-dimensional digital image of an inner surface portion,preferably along the whole circumferential development.

During the rotation, the command and control unit cyclically activates,in rapid alternating sequence, said first, second and third light sourceand activates the linear camera to acquire a respective two-dimensionallinear digital image (colour or monochrome) of the respective linearsurface portion in synchrony with the activation of the first, secondand third light source, respectively. By way of example, every singlelinear digital image comprises 1×2048 pixels in the case of monochromecamera, or 2×2048 pixels in the case of RGB colour or bilinear camera.

By way of example, the time-lag between the acquisition of the first andsecond linear image, as well as between the second and third linearimage and then cyclically between the first and third linear image, isless than 0.2 milliseconds.

Once the desired rotation of the tyre to scan the desired surfaceportion has been carried out, preferably at least one full rotation inorder to acquire all the circular development, a unique digital image isobtained, made with all the linear digital images of the sequence oflinear portions, each illuminated with the three light sources.

The processing unit receives such image from the detection system andseparates therefrom the corresponding first, second and third image ofthe entire desired surface portion.

Such images can be substantially superimposed, pixel by pixel, althoughthe real linear surface portion associated with a single linear imagedoes not exactly corresponds to the three images, due to the rotation ofthe tyre occurred meanwhile. However, the selection of the acquisitionfrequency of the linear images and of the speed of rotation is such thatthe three linear images are mutually interlaced and thus comparablepixel by pixel.

FIG. 3 shows an exemplary visual representation in grey scale of anexample of said first image, that is, of a two-dimensional digitalimage, acquired in the visible frequency range, of an inner surfaceportion of a tyre illuminated with diffused light (totally similar to acommon black and white picture). The circumferential direction of thetyre is arranged along the horizontal direction in the figure (straightline 300).

In the example in FIG. 3, each pixel of the digital image is associatedwith a scalar value (grey level or scale) in a scale of 255 levelsdirectly representative of the reflectivity and/or diffusivity and/orcolour of the inner surface point corresponding to the pixel considered.The present invention may also be applied to digital images in whicheach pixel is associated with a vector value, such as digital colourimages. For example, the method described herein may be carried out oneach channel/colour or combinations thereof, or on a selected channel(e.g. green, which advantageously provides a better image quality).

The digital image on which the method described herein is carried outmay coincide with the digital image directly detected by the detectionsystem or, more preferably, may be subjected, before carrying out themethod described herein, to a pre-processing to improve the qualitythereof. Said pre-processing may comprise one or more of filtering,balancing, noise reductions, smoothing, for example as known in the art.Hereinafter it is assumed that such pre-processing does not change thetwo-dimensional digital nature of the image, such that each pixel isassociated with a luminosity (or tone) value representative of thereflectivity and/or diffusivity and/or colour of the inner surface.

As seen in FIG. 3, the inner surface of the tyre is grooved by aplurality of reliefs that form a “pattern.” The presence of the reliefsproduces a variation in the reflectivity of the inner surface detectedby the camera. The present invention is also applicable to low relief orsimply two-dimensional patterns, i.e. consisting only in a variation ofcolour and/or reflectivity and devoid of depth. Typically, the patterncomprises a series of rectilinear sections 301 substantially mutuallyparallel, typically distributed with substantial periodicity along thecircumferential direction, and a dense network (in the jargon called‘pebble edge’) of substantially rectilinear segments 302 interconnectedin a substantially continuous network, the pattern being typicallycharacterised by a substantial periodicity thereof along thecircumferential direction. It is noted that the pattern develops on theinner surface of the tyre, which is provided with its own curvature.

Preferably, the pattern consists of closed broken lines (polygons)connected to each other. Typically, the pattern consists of polygonsadjacent to each other (e.g. is devoid of isolated polygons).

The pattern has a scheme that is repeated substantially equal thereto ina plurality of positions distributed along the circumferentialdirection, typically with a substantial circumferential periodicity (forexample with a local period variation, in absolute value, falling within5% of the average period calculated on the whole image), even moretypically with continuity along the whole digital image. In the exampleshown, the pebble edge has a circumferential periodicity equal to twicethe periodicity of the rifling, whereby the overall periodicity of thepattern is equal to that of the pebble edge.

As stated said above, reliefs 301, 302 are the imprint left by thepneumatic bladder. In practice, said scheme is typically repeated alongthe circumferential direction with slight variations in the periodicityand/or shape and/or orientation and/or axial position, while remainingsubstantially equal, such variations being due for example to thenon-uniformity of the bladder expansion and/or positioning, and/or tosmall distortions of the pattern imprinted on the bladder itself, and/orto phenomena of distortion in the image detection process (for example,due to faulty centring of the axis of rotation of the tyre, non-perfectcircularity of the tyre, etc.).

For more clarity, FIG. 3 shows a circumferential digital image portionlong, along direction 300, only two and a half times around the periodof the pattern; however, typically the processed digital imagecorresponds to a circumferential inner surface portion comprising saidscheme repeated at least eight-ten times. Preferably, thecircumferential inner surface portion processed covers the entirecircumferential inner development of the tyre.

Typically, the processed digital image corresponds to a portion of theinner surface having a length in the axial direction (the directionperpendicular to direction 300 in FIG. 3) of at least 5 cm, preferablyequal to at least half of the overall axial development of the tyrecrown.

FIG. 3 is shown an exemplary defect 303 (shown enlarged in FIG. 3a whichshows a rotated detail of FIG. 3) consisting in a cut that crosses atleast one segment of the pattern.

Preferably, the method provides for deriving a value representative ofthe pattern period through digital image processing, for example byseeking a maximum of an autocorrelation function (for example, thePearson correlation coefficient calculated on the values associated withthe pixels of the image) between a given portion (in the jargon called‘support’) of the digital image (having adequate dimensions, for examplecircumferential length greater than the period and smaller than threetimes the period) and a plurality of further portions of the digitalimage having dimensions equal the to the dimensions of said given imageportion and arranged in circumferentially distributed positions.Preferably, the circumferential autocorrelation is repeatedly calculatedwith reference to multiple different supports and partially overlappingin the axial direction of the image, and having the same dimensions,with the aim of selecting the most reliable autocorrelation peak toidentify the pattern period. Alternatively, it is provided to acquire apredetermined value of the period, for example from a measurement and/orfrom the specifications of the bladder.

The method involves identifying a first region 304 of the digital imagecorresponding to a sub-portion of the scheme, for example having asmaller circumferential development than an entire circumferentialdevelopment of the scheme (in the example equal to about one third ofthe circumferential development of the scheme, coinciding with saidperiod). The dimensions of the first region are advantageouslyconsistent with the typical expected dimensions of the defect sought.

It is further provided to identify a respective plurality of regions305, 306 of the digital image homologous to the first region 304 anddistributed along the circumferential direction. Each homologous regioncontains a respective scheme sub-portion substantially identical to thescheme sub-portion of the first region. To this end, a correlationfunction is calculated (for example, the Pearson correlationcoefficient) between the first region and a portion of the rest of thedigital image. Preferably, a first homologous region 305 is firstidentified by calculating the correlation function between the firstregion 304 and a plurality of regions having dimensions equal to thefirst region and arranged in a neighbourhood of a point of the digitalimage that is circumferentially distant from the first region by adistance equal to a period P. For example, if the coordinates of thecentre of the first region 304 are x₀, y₀, a region of equal dimensionsis first identified, having the coordinates x₀, y₀+P at the centre.Then, the correlation function is calculated between the first regionand all the regions of the same dimensions whose centre is located inthe neighbourhood of coordinates x₀±Δx, y₀+P±Δy, with Δx, Δy equal to anappropriate number of pixels, for example 5-10 pixels. The region havingcoordinates x₁, y₁ at the centre, at which the correlation functionexhibits a maximum (at least local), is identified as the firsthomologous region 305.

The algorithm is repeated starting from the first homologous region 305and seeking a correlation maximum in the neighbourhood of coordinatesx₁±Δx, y₁+P±Δy, in order to locate the second homologous region 306(having at the centre coordinates x₂, y₂), and so on iteratively, so asto identify a sequence of homologous regions in succession. Inparticular, a tuple of coordinates x_(n), y_(n) is calculated,corresponding to the centre (or any other reference point) of the tupleof homologous regions.

In the example described herein, the acquisition of the valuerepresentative of the pattern period by processing the digital image andidentifying such tuple of coordinates are performed on the image indiffuse light (of the type shown in FIG. 3).

However, the Applicant has verified that even more robust results can beobtained if the calculation operations of the period and/oridentification of such tuple (for example the identification ofhomologous regions through autocorrelation) are carried out on adifference image, in which each pixel is associated with a valuerepresentative of the difference of the corresponding luminosity valuesof the second and third images acquired in grazing light, as describedabove.

This tuple of coordinates is then shown on the first image in diffuselight in order to identify a corresponding first region and acorresponding plurality of homologous regions in said first image.

It is further provided to calculate a model of the scheme sub-portion,in which each pixel is associated with a mean value of the valuesassociated with the pixels of the first region and of the respectivehomologous regions of the first image having the same coordinates ofsaid each pixel.

To this end, for illustration purposes, FIG. 4 shows a 3D graphicalrepresentation of a stack 307 of regions obtained by overlapping of thefirst region (for example at the base of the stack) and all itsrespective homologous regions of the first image in diffuse light. Ascan be seen, each pixel at the base of the stack corresponds to a set ofstatistical values (scalar, or grey scale) lying on the correspondingvertical column. Such statistical set has an intrinsic variance due tothe deformations described above.

Preferably, the digital model 308 of the scheme sub-portion (shown inFIG. 5) associated with the first region 304 is calculated consideringthe median value of the corresponding statistical set for each pixel ofthe base of the stack. A model 308 is thus generated which has in eachrelative coordinate pixel (i,j) the median value (e.g. grey level)calculated on all of the relative coordinate pixels (i,j) of the set ofthe first region and of the homologous regions. As can be seen in FIG.5, the model thus calculated does not contain contributions from anydefects, in addition to exhibiting a high image quality (e.g. in termsof noise and/or sharpness).

Preferably, a digital model of the respective scheme sub-portion iscalculated according to the above for a plurality of first regions, eachcomprising the respective scheme sub-portion. The first regions form aconnected digital image portion having circumferential development aboutequal to the period. In this way, a digital model is calculated for thewhole scheme that makes up the pattern. Preferably, the first regionsare mutually partially overlapping in the axial direction and/or in thecircumferential direction, in order to improve the reliability of themethod.

Once the models of respective sub-portions have been constructed, apattern model is obtained by replacing, in said first image, said modelof the respective scheme sub-portion to each first region and to therespective homologous regions. By the above self-learning procedure, animage is obtained that is a global model of the surface portion withoutdefects, that is easily comparable with the actual image.

For the purpose of this comparison, it is advantageous to use thegradient modulus and orientation as described hereinafter.

In one embodiment, for each pixel of the pattern model in luminosityvalues calculated on the first image, it is contemplated to calculate amodulus value and an orientation value of a gradient of the luminosityvalues associated with the pixels, thereby obtaining a pattern model ingradient modulus values and in gradient orientation values,respectively. In order to reduce the use of computing resources, it ispreferable to calculate these gradient modulus and gradient orientationvalues on each scheme sub-portion model in luminosity values, and thenproceed with the above operation of obtaining a pattern model byreplacement.

In an alternative and preferred embodiment, the operations describedabove for the identification of homologous regions and the calculationof a respective scheme sub-portion model (preferably by the use of atuple of coordinates identified on the difference image) are conductedon the gradient modulus values and on the gradient orientation valuescalculated on the first image in luminosity values. In this case, it isnot strictly necessary to calculate the pattern model in luminosityvalues on the first image.

For the purposes of calculating the gradient modulus and orientationvalues, the gradient of the luminosity values is calculated for eachdigital image pixel along the two horizontal and vertical coordinates ofthe image, thus obtaining a vector with two components: a modulus (e.g.the root of the quadratic sum of the two components) and an orientationin the digital image (e.g. the angle formed by the vector with respectto the horizontal direction), normalized in the range [0-180° ] or[−90°-+90° ].

In order to calculate the gradient modulus and orientation values, it isfor example to proceed as follows: being I(x,y) each pixel of the inputimage (the notation (x,y) is omitted when unnecessary), the following iscalculated:

-   -   Ix=I*Kx, where ‘*’ is the convolution operator, and Kx is an        appropriate kernel for the calculation of the first derivative        in x (e.g. Kx=[1 −1])    -   Iy=I*Ky, where ‘*’ is the convolution operator, and Ky is an        appropriate kernel for the calculation of the first derivative        in y (e.g. Ky=[1; −1])    -   Grad(x,y)=[Ix, Iy]=gradient of I(x,y)    -   gradient module=sqrt((Ix)^2+(Iy)^2)    -   gradient orientation=arctan(Iy/Ix)

FIG. 6 shows an exemplary visualization of a surface sub-portioncorresponding to a central portion of the image in FIG. 3, in which eachpixel is associated with a grey level representative of (e.g.proportional to) the gradient modulus in the considered pixel (e.g.light pixels correspond to a high gradient modulus and vice versa).

FIG. 7 shows an exemplary visualization of the same surface sub-portionin FIG. 6, in which each pixel is associated with a grey level uniquelyrepresentative of the gradient orientation in the pixel considered.

A comparison between FIGS. 6 and 7 shows that the low gradient moduluszones out of the pattern are characterized by a non-significantorientation value (variable disorderly).

As said above, by executing the following operations on the digitalimages as shown in FIGS. 6 and 7: identifying a plurality of firstregions; for each first region, identifying a respective plurality ofhomologous regions; and calculating a respective model, the patternmodel is obtained in gradient modulus values and gradient orientationvalues, respectively.

At this point, the resulting pattern model (be it in luminosity valuesand/or modulus values and/or gradient orientation values) is subjectedto dilation to introduce a tolerance that takes account of thedeformations and/or drifts of the scheme in the pattern.

In a first preferred embodiment, the pattern model in gradient modulusvalues is first binarised (for example by thresholding with a single ordouble threshold) to obtain a binary model of the pattern.

Such binary model of the pattern is subjected to dilation (for exampleby means of a morphological processing operation) in order to obtain adilated pattern binary model in which the pixels have an associatedfirst binary index, the value of which is indicative of whether thepixel belongs or not to the dilated patterns, respectively (in order todistinguish the pixels belonging to the background from those belongingto the dilated pattern the first image by comparison with such a dilatedbinary model).

Moreover, also the gradient orientation values of the pattern model arepreferably subjected to dilation. Preferably, each pixel of the dilatedpattern binary model belonging to the dilated pattern is associated witha second index, the value of which is representative of the orientationof the dilated pattern section passing by the pixel: for example, thegradient orientation value of the pixel having maximum gradient modulusvalue in a predetermined neighbourhood (for example equal to 5-10 pixelsof radius) of said each pixel in said pattern model may be selected.

In a second alternative embodiment, the gradient modulus values and thegradient orientation values of the pattern model are first bothsubjected to dilation. For example, each pixel of the dilated patternmodel is assigned as gradient modulus value the maximum value of thegradient module in a predetermined neighbourhood of said pixel in thepattern model (said neighbourhood exemplarily having a radius equal to5-10 pixels) and as gradient orientation value that associated with thepixel having said maximum gradient modulus value. Subsequently, thepattern model thus obtained is binarised based on the gradient modulusvalue, thereby obtaining the value of said first index.

FIG. 8 shows an example of pattern model obtained as a result of saidoperations in the two embodiments. Each pixel of the image in FIG. 8 notbelonging to the dilated pattern but to the pattern background (forexample having value of said first index equal to zero) is shown inblack. Each pixel belonging to the dilated pattern (for example havingvalue of said first index equal to one) is shown in a grey level otherthan black, where each grey level biuniquely corresponds to a value ofthe second index representative of the orientation of the correspondingsegment of the dilated pattern passing by the considered pixel. As canbe seen, the pixels outside the dilated pattern (black pixels) do nothave an associated significant value of the second index.

Before making the comparison between the first image of the tyreacquired in diffuse light into luminosity values and the dilated patternmodel as obtained above, it is advantageous to process such first imagein order to highlight the potential defects of the rest of the image.

To support the description of the method for highlighting potentialdefects, reference will be made to FIGS. 9-12.

FIG. 9 exemplarily shows a visual representation in greyscale of afurther example of a portion of the first image, of a similar nature tothat shown in FIGS. 3 and 3 a, showing a further inner surface portionof a tyre illuminated with diffuse light, in which a defect 903 ispresent, consisting of a cut that crosses some segments of the pattern.On the surface portion in FIG. 9 there were traces of a release agentused in the bladder, in particular on the walls of the pattern segments.Since this release agent is highly reflective, such walls of the patternsegments in the image give rise to a pair of very bright parallel lines904. A darker line 905 is present between the two bright lines whichcorresponds to the ‘top’ of the pattern segments, less dirty withrelease agent. In such a situation, in principle, segments 905 may beconfused with cuts 903 as they have similar luminosity features.

In order to highlight the potential defects from the rest of the image,it is contemplated to calculate a value representative of the maineigenvalue (or maximum eigenvalue) of the Hessian matrix in said pixelfor each pixel of the first image in luminosity values.

By way of example, the Hessian matrix is calculated as follows. Being,as above, I(x,y) each pixel of the input image, the following iscalculated:

-   -   Ixx=I*Kxx, where ‘*’ is the convolution operator, and Kxx is an        appropriate kernel for the calculation of the second derivative        in x (e.g. Kxx=[1 −2 1] row vector)    -   Iyy=I*Kyy, where ‘*’ is the convolution operator, and Kyy is an        appropriate kernel for the calculation of the second derivative        in y (e.g. Kyy=[1; −2; 1] column vector)    -   Ixy=Iyx=I*Kxy where ‘*’ is the convolution operator and Kxy is        an appropriate kernel for the calculation of the mixed        derivative        (e.g. the matrix Kxy=[1 0 −1 0 0 0; −1 0 1] where “,” is the        line separator).

The Hessian H=[Ixx Ixy; Iyx Iyy] is thus obtained.

From the Hessian matrix H, the eigenvectors and the eigenvalues arecalculated, for example by the algorithm EVD (Eigen Value/VectorDecomposition).

Preferably, the main eigenvalue values are binarised by comparison witha first threshold value, in order to identify the proposed defect pixellike those pixels whose maximum eigenvalue associated is above suchfirst threshold value.

FIG. 10 shows an exemplary visualisation of the first image portionsubstantially corresponding to that shown in FIG. 9, binarised on thebasis of the main eigenvalue. It is noted that said finalisation hasenhanced both the pixels belonging to cut 903 and the pixels at walls904 (more precisely of the transition areas between the patternbackground and walls 904) and of the central areas 905 of the patternsegments 905 where the maximum eigenvalue is high.

In order to eliminate at least the pixels in the transition areasbetween the pattern background and the walls of the pattern segmentsfrom the proposed defects thus obtained, as well as other spuriouspixel, it is contemplated to compare the gradient modulus valuescalculated on the luminosity values of the first image with a secondthreshold value and identify the pixels with a gradient modulus valuesmaller than such second threshold value as proposed defects.

FIG. 11 substantially shows the same image portion in FIG. 9 wherein thepixels have been binarised on the basis of the gradient modulus(optionally with appropriate edge smoothing operations). The whitepixels are those with low gradient modulus value, i.e. smaller than thesecond threshold value. It is noted that pixels 904 at the edges of thepattern segments have a high gradient value.

FIG. 12 substantially shows the same image portion in FIG. 10, where thewhite pixels are the proposed defects obtained from the concurrentthresholding with said two threshold values (i.e. the white pixels bothin FIG. 10 and in FIG. 11). It is noted that some pixels 905 remain asproposed defects at the centre of some pattern sections, while almostall pixels at the edges of the pattern sections have been substantiallyfiltered by the criterion. In order to further select the proposeddefects, a third selection criterion is preferably provided, preferablyused in combination with said two further criteria, based on theluminosity value in the image acquired in diffuse light. In particular,such luminosity value is compared with a third threshold value and allthe proposed defect pixels as identified above are selected, which alsohave the luminosity value below the third threshold value.

At this point, it is possible to make the comparison between theproposed defect pixels thus selected with the dilated pattern model freefrom defects as calculated above, in order to detect any defects in thesurface portion as a function of such comparison.

Preferably, a first set of pixels is identified among said proposeddefect pixels for which the corresponding pixels in the dilated patternmodel do not belong to the dilated pattern (for example with referenceto FIG. 8, the black ‘background’ areas), to identify the defects (ordefect portions) located outside the pattern, i.e. belonging to thepattern ‘background’. Preferably, it is contemplated to identify asecond set of pixels among said proposed defect pixels that have thecorresponding pixel in the pattern model belonging to the pattern (forexample the pixels in grey level other than black in FIG. 8), for whichthe at least local orientation of the pattern section passing by saiddilated pattern model (represented by said second value, e.g. theorientation value of the dilated gradient) is significantly different(for example, the difference is greater than 20°) from the orientationof the section formed by said proposed defects and passing by saidpixel. In that case, such proposed defect section passing by said pixelis probably a cut having a portion that intersects with a patternsection in said dilated pattern model.

In a first embodiment, the orientation of the section formed by saidproposed defects and passing by said pixel is represented by thegradient orientation value in said pixel calculated as described aboveon the luminosity values in the first image in diffuse light.

In a second embodiment, the orientation of the section formed by saidproposed defects and passing by said pixel is represented by theorientation of the main eigenvector of the Hessian matrix in said pixelcalculated as described above on the luminosity values in the firstimage in diffuse light.

Preferably, it is contemplate to merge (in OR logic) the first andsecond set of pixels to form a final image (typically binary)representative of said surface portion wherein the pixels correspondingto said first and second set are distinct from the remaining pixels, asexemplarily shown in FIG. 13, where it is seen that both the cuttingportion at the pattern background and the cut portion at the dilatedpattern has been identified.

Preferably, appropriate morphological processing can be carried out onsaid pixels in order to discard false proposed defects. For example, theisolated pixels blocks or the connected pixel regions (‘blob’) that arenot compatible with the defects sought are eliminated; in particular,the selection can be made by area and/or length. FIG. 14 shows the endresult of a processing of this type carried out on the image in FIG. 13.

FIG. 15 shows a flowchart of the method of the present invention inwhich operation 1010 represents the operation of providing the tyrehaving a surface with a pattern with a set of linear elements.

Operation 1020 represents the operation of acquiring at least onedigital image of a portion of the surface, said digital image comprisinga structure comprising sections representative of said linear elementsof the pattern in said surface portion and representative of possibleelongated defects, said sections of said structure having, at leastlocally, a respective orientation.

Operation 1030 represents the operation of providing a digital model ofthe pattern in said surface portion, wherein each pixel is associatedwith a first index representative of whether the pixel belongs to apattern section in said model and, for the pixels belonging to thepattern in said model, a second index representative of an at leastlocal orientation of said pattern section in said model passing throughsaid pixel.

Operation 1040 represents the operation of calculating, for each pixelof said structure, a third index representative of the orientation ofthe structure section passing through said pixel.

Operation 1050 represents the operation of comparing, for each pixel ofthe structure having a corresponding pixel in the pattern modelbelonging to the pattern, said third index with said second indexassociated with said corresponding pixel in the pattern model. Operation1060 represents the operation of establishing whether said pixel of thestructure belongs to a proposed defect on the basis of said comparison.

The invention claimed is:
 1. A method for detecting one or more possibleelongated defects on an inner or outer surface of a tyre, comprising:placing the tyre on a support, wherein the inner or outer surface of thetyre comprises a pattern of linear elements; subjecting, by a commandand control unit, the tyre to a rotation around its axis of symmetry;providing to a processor a digital model of the pattern, wherein thedigital model of the pattern comprises one or more pixels and one ormore pattern sections, wherein each pixel of the digital model of thepattern is associated with a first index indicating whether the pixelbelongs to the one or more pattern sections and wherein when therespective pixel belongs to the one or more pattern sections, the pixelis associated with a second index of an orientation of the one or morepattern sections; acquiring, by a detection system, one or more digitalimages of the inner or outer surface, wherein the detection systemcomprises a camera, wherein the digital images comprise a structurecomprising one or more structure sections corresponding to the linearelements and to the one or more possible elongated defects, wherein thestructure comprises one or more pixels of the structure; calculating, bythe processor, for each pixel of the structure, a third index of anorientation of the structure sections to which the respective pixelbelongs; for each pixel of the structure having a corresponding pixel inthe digital model of the pattern belonging to the one or more patternsections of the digital model, comparing, by the processor, the thirdindex with the second index; and establishing, by the processor, if thepixel of the structure belongs to the one or more structure sectionscorresponding to the one or more possible elongated defects based on thecomparison.
 2. The method as claimed in claim 1, wherein the first indexis binary.
 3. The method as claimed in claim 1, further comprisingidentifying, by the processor, a first set of pixels of the structurewhose corresponding pixels in the digital model of the pattern do notbelong to any pattern sections, and a second set of pixels of thestructure consisting of the pixels of the structure belonging to the oneor more structure sections corresponding to the one or more possibleelongated defects.
 4. The method as claimed in claim 3, furthercomprising creating a final image comprising one or more pixels of theinner or outer surface, wherein the pixels of the final image whichcorrespond to the first and second set of pixels are discriminated fromany remaining pixels of the final image.
 5. The method as claimed inclaim 1, wherein calculating the third index for each pixel of thestructure comprises calculating a gradient in each of the pixels of thestructure, wherein the gradient comprises a modulus and an orientationof the gradient in the digital images.
 6. The method as claimed in claim5, wherein the orientation of the gradient is an angle formed by thegradient with respect to a reference direction and wherein the thirdindex is representative of the orientation of the gradient.
 7. Themethod as claimed in claim 1, wherein calculating the third index foreach pixel of the structure comprises calculating at least oneeigenvector of a Hessian matrix in each pixel of the structure, thethird index being an orientation of the at least one eigenvector.
 8. Themethod as claimed in claim 7, wherein the at least one eigenvector is aprincipal eigenvector of the Hessian matrix.
 9. The method as claimed inclaim 1, wherein the digital model of the pattern contains a dilatedpattern which is dilated with respect to the pattern in the inner orouter surface.
 10. The method as claimed in claim 1, wherein comparingthe third index with the second index comprises calculating an angulardifference between the orientation of structure section and theorientation of the corresponding pattern section.
 11. The method asclaimed in claim 10, wherein the respective pixels of the structure areestablished to belong to the one or more structure sectionscorresponding to the possible elongated defects if the angulardifference exceeds, in absolute value, 5°.
 12. The method as claimed inclaim 1, wherein the linear elements of the pattern are rectilinearsegments interconnected to form a network of polygons.
 13. The method asclaimed in claim 1, wherein acquiring the digital images comprisesacquiring a first digital image, wherein each pixel of the first digitalimage is associated to a luminosity value representative of reflectivityand/or diffusivity and/or colour of a surface point corresponding toeach pixel of the first digital image, the surface point illuminatedwith diffused light.